10 Python OOP Best Practices

In this article, we will be discussing 10 best practices when working with Python’s OOP features. Python is a very popular language when it comes to OOP and provides great flexibility when working with classes and objects.

However, as with any language, there are certain best practices that should be followed in order to write clean, maintainable, and efficient code. By following these best practices, you can avoid common mistakes and write code that is easier to work with and debug.

1. Use Python 3

Python 3 is the latest major version of Python, and it’s not backward-compatible with older versions. That means code written in Python 3 will not run in Python 2.7 (or lower).

If you’re starting a new project, there’s no reason to use an older version of Python. Python 3 has been around for over 10 years now, and all the major libraries have been ported to it.

There are still some holdouts, but they’re getting rarer every day. For example, Django 1.11 (the current LTS release) only supports Python 3.4+. So if you’re using Django, you should be using Python 3.4+, which was released in 2014.

If you’re stuck on an older version of Python for some reason, you can use the six library to write code that’s compatible with both Python 2 and 3. But even then, you should be aiming to migrate your code to Python 3 as soon as possible.

2. Write Docstrings for Every Function, Class, and Module

Docstrings are a way of documenting your code so that other people can understand what it does. They are especially important in Python OOP because they provide a clear and concise description of the purpose of each class and function.

Not only do docstrings make your code easier to understand, but they also help with testing and debugging. When you’re trying to figure out why something isn’t working, having a well-written docstring can be a lifesaver.

Finally, docstrings are a great way to communicate with other developers on your team. If you’re working on a project with multiple people, writing docstrings for your classes and functions is a good way to let others know what you’re doing and why.

3. Name Your Classes and Functions Consistently

If you name your classes and functions inconsistently, it will be difficult for other developers (and yourself) to understand what they do. For example, if you have a function named “get_user” in one class and “fetch_user” in another, it will be confusing to know which one does what.

It’s important to be consistent with your naming because it makes your code more readable and maintainable. It also makes it easier to search for things when you need to. So, take the time to choose good names for your classes and functions, and stick to them.

4. Use One Statement per Line

When you use one statement per line, it’s easier to read your code. Each line is a self-contained unit of code that does one thing and one thing only. This makes your code more understandable and maintainable.

It’s also easier to debug your code when you use one statement per line. If there’s an error in your code, it’s usually easier to find if each line is a self-contained unit of code.

Finally, using one statement per line can make your code more efficient. When you have multiple statements on one line, Python has to parse all of those statements before it can execute any of them. But when you have one statement per line, Python can execute each statement as soon as it’s parsed.

So if you want to write Python code that’s easy to read, maintain, and debug, make sure you use one statement per line.

5. Limit Your Lines of Code to 79 Characters

When you limit your lines of code to 79 characters, it forces you to be more concise and clear in your code. This is because you have less room to work with, so you can’t include any unnecessary or superfluous code.

This best practice also makes your code more readable, since shorter lines are easier to scan and understand. It also helps prevent merge conflicts, since shorter lines are less likely to be changed in different versions of the code.

Finally, limiting your lines of code to 79 characters can help improve your team’s productivity, since they won’t have to waste time scrolling horizontally to read your code.

6. Avoid Comparing Boolean Values to True or False Using ==

When you compare a boolean value to true or false using ==, Python will first convert the boolean value to an integer (1 for True, 0 for False), and then compare the integers. This is not what you want! You want to compare the boolean values directly.

The correct way to compare boolean values is by using the “is” operator:

if my_bool is True:
# do something

If you absolutely must use ==, make sure you compare the boolean value to the literal value True:

if my_bool == True: # this is OK
# do something

But again, it’s better to use “is”.

7. Don’t Check for Empty Values (and Other Falsy Values) Using len()

When you check for empty values using len(), you are actually checking for the length of the object. This means that if the object is a list, tuple, set, or dictionary, it will return the number of items in that object.

However, there are other objects in Python that are also considered “falsy” values, such as None, 0, False, and an empty string. So, if you’re only checking for len() == 0, you might end up with some unexpected results.

Instead, it’s better to use the “in” operator, which will return True if the value is in the object, and False if it is not.

For example, let’s say you have a list of numbers, and you want to know if the list is empty:

numbers = [1, 2, 3]

if len(numbers) == 0:
print(“The list is empty”)
else:
print(“The list is not empty”)

This code will correctly print “The list is not empty”, since the list is not empty.

But what if we change the list to be an empty string?

numbers = “”

if len(numbers) == 0:
print(“The list is empty”)
else:
print(“The list is not empty”)

Now, this code will incorrectly print “The list is not empty”, since the list is actually empty.

To fix this, we can use the “in” operator:

numbers = “”

if numbers:
print(“The list is not empty”)
else:
print(“The list is empty”)

This code will correctly print “The list is empty”, since the list is empty.

8. Use List Comprehensions Instead of map() and filter()

List comprehensions are more concise and easier to read than map() and filter(). They also run faster because they’re written in C, which is the language that Python is written in.

Map() and filter() are still useful in some situations, but for most cases, list comprehensions are the way to go.

9. Prefer enumerate Over range

When working with Python’s OOP, you’ll often find yourself needing to iterate over a list of objects. The most common way to do this is with a for loop:

for i in range(len(my_list)):
print(my_list[i])
However, there’s a better way to do this using the enumerate function:

for i, obj in enumerate(my_list):
print(obj)
The enumerate function returns an iterator that yields pairs of values: the index and the value from the list. This is much more convenient than using the range function, and it makes your code more readable as well.

10. Take Advantage of Each Block in try/except/else/finally

The try block is for code that might throw an exception. The except block is for code that will handle the exception if it’s thrown. The else block is for code that will run if no exception is thrown. And finally, the finally block is for code that will always run, whether or not an exception is thrown.

If you’re only using the try block, then you’re not taking full advantage of Python’s exception-handling capabilities. By using all four blocks, you can write more robust and reliable code.